D
C. Rother, Kumar, S., Kolmogorov, V., and Blake, A.,
“Digital tapestry [automatic image synthesis]”, in
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 589–596.
P. Bell and Ommer, B.,
“Digital Connoisseur? How Computer Vision Supports Art History”, in
Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.), Rome: Artemide, 2016.
A. Vijayan, Tofanelli, R., Strauss, S., Cerrone, L., Wolny, A., Strohmeier, J., Kreshuk, A., Hamprecht, F. A., Smith, R. S., and Schneitz, K.,
“A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule”,
eLife, 2021.
D. Cremers, Schnörr, C., and Weickert, J.,
“Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework”, in
IEEE First Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada, 2001, pp. 237–244.
D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C.,
“Diffusion Snakes Using Statistical Shape Knowledge”, in
Proc. Algebraic Frames for the Perception-Action Cycle, Kiel, 2000, vol. 1888, pp. 164–174.
D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C.,
“Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”,
Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.
H. Spies, Haußecker, H., Jähne, B., and Barron, J. L.,
“Differential range flow estimation”, in
Proceedings of the 21th DAGM Symposium on Pattern Recognition, 1999, p. 309--316.
J. A. J. Steen, Steen, H., Georgi, A., Parker, K. C., Springer, M., Kirchner, M., Hamprecht, F. A., and Kirschner, M. W.,
“Different Phosphorylation States of the Anaphase Promoting Complex in Response to Anti-Mitotic Drugs: A Quantitative Proteomic Analysis”,
Proceedings of the National Academy of Sciences, vol. 105, pp. 6069-6074, 2008.
Technical Report (173.02 KB) F. A. Hamprecht, Cohen, A. J., Tozer, D. J., and Handy, N. C.,
“Development and assessment of new exchange-correlation functionals”,
Journal of Chemical Physics, vol. 109, pp. 6264-6271, 1998.
X. Lou, Kirchner, M., Renard, B. Y., Köthe, U., Graf, C., Lee, C., Steen, J. A. J., Steen, H., Mayer, M. P., and Hamprecht, F. A.,
“Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments”,
Bioinformatics, vol. 26(12), pp. 1535-1541, 2010.
Technical Report (518.01 KB) E. Eyjolfsdottir, Branson, S., Burgos-Artizzu, X. P., Hoopfer, E. D., Schor, J., Anderson, D. J., and Perona, P.,
“Detection of social actions in fruit flies”,
Lecture Notes in Computer Science, vol. 8690, pp. 772–787, 2014.
S. Ramos, Gehrig, S., Pinggera, P., Franke, U., and Rother, C.,
“Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling”, in
IEEE Intelligent Vehicles Symposium, Proceedings, 2017, pp. 1025–1032.
C. Decker and Hamprecht, F. A.,
“Detecting individual body parts improves mouse behavior classification”, in
Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). Proceedings, 2014.
Technical Report (1.48 MB) J. Schlecht, Carque, B., and Ommer, B.,
“Detecting Gestures in Medieval Images”, in
Proceedings of the International Conference on Image Processing, 2011, p. 1309--1312.
Technical Report (1.61 MB) A. Bruhn, Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., and Schnörr, C.,
“Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing”, in
Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 290–297.